Single Cell Transcriptional Profiling of Adult Mouse Cardiomyocytes

被引:8
作者
Flynn, James M. [1 ]
Santana, Luis F. [2 ]
Melov, Simon [1 ]
机构
[1] Buck Inst Res Aging, Novato, CA 94945 USA
[2] Univ Washington, Dept Physiol & Biophys, Seattle, WA 98195 USA
来源
JOVE-JOURNAL OF VISUALIZED EXPERIMENTS | 2011年 / 58期
基金
美国国家卫生研究院;
关键词
Molecular Biology; Issue; 58; Single cell analysis; Microarray; Gene expression; Cardiomyocyte; Mouse heart perfusion; mice; qPCR; STOCHASTIC GENE-EXPRESSION;
D O I
10.3791/3302
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
While numerous studies have examined gene expression changes from homogenates of heart tissue, this prevents studying the inherent stochastic variation between cells within a tissue. Isolation of pure cardiomyocyte populations through a collagenase perfusion of mouse hearts facilitates the generation of single cell microarrays for whole transcriptome gene expression, or qPCR of specific targets using nanofluidic arrays. We describe here a procedure to examine single cell gene expression profiles of cardiomyocytes isolated from the heart. This paradigm allows for the evaluation of metrics of interest which are not reliant on the mean (for example variance between cells within a tissue) which is not possible when using conventional whole tissue workflows for the evaluation of gene expression (Figure 1). We have achieved robust amplification of the single cell transcriptome yielding micrograms of double stranded cDNA that facilitates the use of microarrays on individual cells. In the procedure we describe the use of NimbleGen arrays which were selected for their ease of use and ability to customize their design. Alternatively, a reverse transcriptase - specific target amplification (RT-STA) reaction, allows for qPCR of hundreds of targets by nanofluidic PCR. Using either of these approaches, it is possible to examine the variability of expression between cells, as well as examining expression profiles of rare cell types from within a tissue. Overall, the single cell gene expression approach allows for the generation of data that can potentially identify idiosyncratic expression profiles that are typically averaged out when examining expression of millions of cells from typical homogenates generated from whole tissues.
引用
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页数:13
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